Image Representation using Bag of Perceptual Curve Features

Abstract

There are many applications such as augmented or mixed reality with limited training data and computing power which results in inapplicability of convolutional neural networks in those domains. In this method, we have extracted the perceptual edge map of the image and grouped its perceptual structure-based edge elements according to gestalt psychology. The connecting points of these groups, called curve partitioning points (CPPs), are descriptive areas of the image and are utilized for image representation. In this method, the global perceptual image features, and local image representation methods are combined to encode the image according to the generated bag of CPPs using the spatial pyramid matching. The experiments on multi-label and single-label datasets show the superiority of the proposed method.